Objective: This study aims to examine the influence of surgical patients' and caregivers' general attitudes toward artificial intelligence (AI) on their perceptions of AI-assisted healthcare. Material and Methods: A cross-sectional descriptive study was conducted with 450 participants (240 patients and 210 caregivers) in the surgical clinics of a university hospital. Data were collected face-to-face between AugustDecember 2024 using the Sociodemographic Characteristics Form, the Opinion Questionnaire on AI-Assisted Healthcare, and the General Attitudes Towards AI Scale. Descriptive statistics, chi-square analysis, oneway analysis of variance, and multivariate linear regression analysis were employed for data analysis. Results: A total of 62.91% of patients and 63.8% of caregivers expressed concerns about AI-based robotic surgeons operating without human intervention. Additionally, 54.5% of patients and 48.5% of caregivers did not want AI-based robotic nurses to perform selfcare tasks. 51.6% of patients and 55.2% of caregivers reported that they did not find AI-based robot nurses trustworthy; 52.9% of patients and 52.3% of caregivers reported that they were concerned about AI-based robot nurses assisting surgery in the operating room. Regression analysis indicated that patients' positive attitudes and education levels accounted for 18% of their views on AI-based healthcare services, while caregivers' attitudes, gender, and education levels explained 25%. Male caregivers demonstrated greater acceptance and trust in AI technologies. Conclusion: Both patients and caregivers expressed reservations about AI-assisted healthcare services, emphasizing the importance of human interaction in medical settings. Enhancing education and awareness of AI's potential benefits may support the seamless integration of these technologies within a patient-centered framework.
Keywords: Artificial intelligence; nursing; surgery; robots; healthcares
Amaç: Bu çalışma, cerrahi hastalarının ve bakım verenlerinin yapay zekâya (YZ) yönelik genel tutumlarının yapay destekli sağlık hizmet algıları üzerindeki etkisini incelemeyi amaçlamaktadır. Gereç ve Yöntemler: Araştırma kesitsel ve tanımlayıcı tipte olup, bir üniversite hastanesinin cerrahi kliniklerindeki 450 (240-hasta ve 210-bakım veren) katılımcı ile yapıldı. Veriler, Sosyodemografik Özellikler Formu, Yapay Zekâ Destekli Sağlık Hizmetleri Hakkında Görüş Anketi ve Yapay Zekâya Yönelik Genel Tutumlar Ölçeği ile yüz yüze Ağustos-Aralık 2024 tarihleri arasında toplandı. Veri analizinde tanımlayıcı istatistikler, ki-kare analizi, tek yönlü varyans analizi ve çok değişkenli doğrusal regresyon analizi kullanıldı. Bulgular: Hastaların %62.91'i; hasta yakınlarının %63,8'i insan müdahalesi olmadan çalışan YZ tabanlı robot cerrahlar konusunda endişe duymaktaydı. Hastaların %54,5'i bakım verenlerin %48,5'si YZ tabanlı robot hemşirelerin öz bakım ihtiyaçlarını karşılamasını istememiştir. Hastaların %51,6'sı ve bakım verenlerin %55,2'si YZ tabanlı robot hemşireleri güvenilir bulmadıklarını; hastaların %52,9'u, bakım verenlerin %52,3'ü ameliyathanede ameliyata yardımcı olan YZ tabanlı robot hemşireler konusunda endişeli olduğunu bildirmiştir. Regresyon analizi, hastaların olumlu tutumlarının ve eğitim seviyelerinin YZ destekli sağlık hizmetleri hakkındaki görüşlerinin %18'ini açıkladığını, bakım verenlerin tutumlarının, cinsiyetlerinin ve eğitimlerinin ise %25'ini açıkladığını ortaya koymuştur. Erkek bakım verenlerin YZ teknolojilerini kabullenme ve bu teknolojilere güvenme düzeylerinin daha yüksek olduğu saptandı. Sonuç: Hem hastalar hem bakım verenler YZ destekli sağlık hizmetlerine yönelik olumsuz düşüncelere sahiptir. Özellikle sağlık uygulamalarında insan etkileşiminin önemini vurgulamışlardır. YZ'nin potansiyel faydaları konusunda eğitim ve farkındalığın artırılması, hasta odaklı bir yaklaşım benimsenerek bu teknolojilerin daha uyumlu bir şekilde uygulanmasına yardımcı olabilir.
Anahtar Kelimeler: Yapay zekâ; hemşirelik; cerrahi; robotlar; sağlık hizmetleri
- Faiyazuddin M, Rahman SJQ, Anand G, Siddiqui RK, Mehta R, Khatib MN, et al. The impact of artificial intelligence on healthcare: a comprehensive review of advancements in diagnostics, treatment, and operational efficiency. Health Sci Rep. 2025;8(1):e70312. [PubMed] [PMC]
- St John A, Cooper L, Kavic SM. The role of artificial intelligence in surgery: what do general surgery residents think? Am Surg. 2024;90(4):541-9. [Crossref] [PubMed]
- Aggarwal R, Farag S, Martin G, Ashrafian H, Darzi A. Patient perceptions on data sharing and applying artificial intelligence to health care data: cross-sectional survey. J Med Internet Res. 2021;23(8):e26162. [Crossref] [PubMed] [PMC]
- Beets B, Newman TP, Howell EL, Bao L, Yang S. Surveying Public perceptions of artificial intelligence in health care in the United States: systematic review. J Med Internet Res. 2023;25:e40337. [Crossref] [PubMed] [PMC]
- Suman A, Suman P, Padhy S, Kumar N, Singh A. Healthcare revolution: advances in AI-driven medical imaging and diagnosis. Responsible and Explainable Artificial Intelligence in Healthcare. New York: Academic Press; 2025. p.155-82. [Crossref]
- Bhandari A, Purchuri SN, Sharma C, Ibrahim M, Prior M. Knowledge and attitudes towards artificial intelligence in imaging: a look at the quantitative survey literature. Clin Imaging. 2021;80:413-9. [PubMed]
- Khullar D, Casalino LP, Qian Y, Lu Y, Krumholz HM, Aneja S. Perspectives of patients about artificial intelligence in health care. JAMA Netw Open. 2022;5(5):e2210309. [PubMed] [PMC]
- Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, et al. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640-6. [PubMed]
- Palmisciano P, Jamjoom AAB, Taylor D, Stoyanov D, Marcus HJ. Attitudes of patients and their relatives toward artificial intelligence in neurosurgery. World Neurosurg. 2020;138:e627-e633. [Crossref] [PubMed]
- Stai B, Heller N, McSweeney S, Rickman J, Blake P, Vasdev R, et al. Public perceptions of artificial intelligence and robotics in medicine. J Endourol. 2020;34(10):1041-8. [Crossref] [PubMed] [PMC]
- Shi J, Wei S, Gao Y, Mei F, Tian J, Zhao Y, et al. Global output on artificial intelligence in the field of nursing: A bibliometric analysis and science mapping. J Nurs Scholarsh. 2023;55(4):853-63. [Crossref] [PubMed]
- Jonathan S. Nursing in the digital age: the importance of health technology and its advancement in nursing and healthcare. In: Digital Technology in Public Health and Rehabilitation Care. 1st ed. Academic Press; 2024. p.283-96. [Crossref]
- Almagharbeh WT. The impact of AI-based decision support systems on nursing workflows in critical care units. Int Nurs Rev. 2025;72(2):e13011. [Crossref] [PubMed]
- Martinez-Ortigosa A, Martinez-Granados A, Gil-Hernández E, Rodriguez-Arrastia M, Ropero-Padilla C, Roman P. Applications of artificial ıntelligence in nursing care: a systematic review. J Nurs Manag. 2023;2023:3219127. [Crossref] [PubMed] [PMC]
- Ng ZQP, Ling LYJ, Chew HSJ, Lau Y. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. J Nurs Manag. 2022;30(8):3654-3674. [Crossref] [PubMed]
- Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, et al. Attitudes and perception of artificial intelligence in healthcare: a cross-sectional survey among patients. Digit Health. 2022;8:20552076221116772. [Crossref] [PubMed] [PMC]
- Kandemir F, Azizoğlu F, Terzi B. Hemşirelikte yapay zekâ ve robot teknolojilerinin kullanımı [Use of artificial intelligence and robotic technologies in nursing]. Yoğun Bakım Hemşireliği Dergisi. 2023;27(2):118-27. [Link]
- McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276-82. [Crossref] [PubMed] [PMC]
- Schepman A, Rodway P. Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput Hum Behav Rep. 2020;1:100014. [PubMed] [PMC]
- Kaya F, Aydın F, Schepman A, Rodway P, Yetisensoy O, Kaya M. The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int J Hum Comput Interact. 2024;40(2):497-514. [Crossref]
- Gao S, He L, Chen Y, Li D, Lai K. Public perception of artificial ıntelligence in medical care: content analysis of social media. J Med Internet Res. 2020;22(7):e16649. [Crossref] [PubMed] [PMC]
- Parry MW, Markowitz JS, Nordberg CM, Patel A, Bronson WH, DelSole EM. Patient perspectives on artificial intelligence in healthcare decision making: a multi-center comparative study. Indian J Orthop. 2023;57(5):653-65. [Crossref] [PubMed] [PMC]
- Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, et al. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med. 2021;4(1):140. [Crossref] [PubMed] [PMC]
- Yakar D, Ongena YP, Kwee TC, Haan M. Do people favor artificial intelligence over physicians? A survey among the general population and their view on artificial intelligence in medicine. Value Health. 2022;25(3):374-81. [Crossref] [PubMed]
- Esin H, Karaali C, Teker K, Mergen H, Demir O, Aydogan S, et al. Patients' perspectives on the use of artificial intelligence and robots in healthcare. Bratisl Lek Listy. 2024;125(8):513-8. [PubMed]
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